Choosing between deep learning, gradient-boosted trees, or simpler heuristic models.
: Provides a structured methodology for tackling any ML design question, from requirement clarification to deployment. Real-World Examples Choosing between deep learning
: Design for scalable deployment, handling distribution shifts, and continuous monitoring. Key Case Studies Covered handling distribution shifts
: Unlike academic textbooks, this guide focuses on real-world scalability, data pipelines, and maintenance. this guide focuses on real-world scalability